Development of methods for the analysis of large-scale data sets
In this research area, we apply and develop methods for the analyses of large-scale data sets. Our research is particularly focused on the utilization of genome-scale metabolic models which have been shown to be very useful in interpreting large-scale data sets. Depending on the target organism, we either use publicly available genome-scale metabolic networks or reconstruct them ourselves. Moreover, for the comparative analysis of data across several organisms, we have developed process-based methods that extend the consideration of differentially expressed genes to the detection of processes that are differentially expressed between different states. Using such methods we are able to detect common changes in expression even across very distantly related organisms since such changes are often much more conserved on a functional rather than the genetic level. For microbial organisms, we have extended this approach to the reconstruction of so-called cellular resource allocation models that allow us to map expression data to drastically simplified models of cells. Thereby, we are able to understand how an organism responds to perturbations on a truly systemic level.
The focus of this research area is the elucidation of mechanisms by which organisms improve their ability to react to changes in environmental conditions and the analysis of factors that drive the emergence of microbial cooperation.
In the first area, we focus both on the optimal response to changes in conditions and the preparation for such changes in stochastic environments. Using drastically simplified models of individual pathways or entire cells, we use optimization approaches to identify regulatory programs that are optimally suited to switch between conditions. Building on these analyses we derive hypotheses that can subsequently be validated either using publicly available data or experiments. Regarding strategies that prepare microorganisms for changes in conditions, we are particularly interested in the trade-off between growth and flexibility. This trade-off is exemplified by the two conflicting objectives inherent to microbial growth - the maximization of growth rate in a particular environment and the maximization of the ability to quickly rearrange metabolism after a change in conditions. Our research shows that both factors are important antagonistic forces that shape the allocation of cellular resources. In this area, we are closely collaborating with the group of Jakob Møller-Jensen from Syddansk Universitet (Odense, Denmark) with the aim to understand how these antagonistic forces are controlled during infection processes.
In the second area, we are interested in the identification of factors that drive the emergence of microbial cooperation. Our research, which is conducted in close collaboration with the group of Christian Kost (Max-Planck-Institute for Chemical Ecology, Jena), has shown that metabolic interdependencies between bacteria are much more frequent than expected. Building on these results we aim to identify factors the fitness benefits that organisms gain by giving up their metabolic autonomy.
Systems biology of aging
While aging is an ubiquitous phenomenon affecting nearly all organisms, its underlying mechanisms are still poorly understood. Using large-scale data sets from different organisms, we aim to elucidate common changes during aging. Moreover, we are also interested in how interventions that are known to affect life and health span change the age-specific regulation. On the modeling site, we translate effects that we observe into individual-based models that allow us to study the relevance of these effects on a population level. Translating results from these models back to individual organisms, we are also particularly interested in how different aspects of life-history such as nutritional status and physical exercise affect health parameters such as life-span and the incidence of age-related diseases.